U.S. patent number 11,405,800 [Application Number 17/261,388] was granted by the patent office on 2022-08-02 for system and method for 3d propagation modelling for planning of a radio network.
This patent grant is currently assigned to Jio Platforms Limited. The grantee listed for this patent is Reliance Jio Infocomm Limited. Invention is credited to Gaurav Dalwadi, Deepak Gupta, Nekiram Khosaya, Brijesh I Shah.
United States Patent |
11,405,800 |
Gupta , et al. |
August 2, 2022 |
System and method for 3D propagation modelling for planning of a
radio network
Abstract
A system and method for 3D propagation modelling for planning of
a radio network, is disclosed. In the present invention, automatic
tuning of propagation path loss parameters of a Continuous Wave
(CW) based 3D propagation model for LOS (line of sight) and NLOS
(non-line of sight) radio transmissions in a first geographical
area, is performed. Further, in the present invention, 3D
propagation models for remaining geographies having similar
geographical area and clutter types as the first geographical area,
are generated without performing any drive test while compensating
the propagation path loss parameters of the generated model using
periodically measured user equipment (UE) data. The generated 3D
models may be updated dynamically as the 3D models are developed
based on UE data updated from time to time.
Inventors: |
Gupta; Deepak (Navi Mumbai,
IN), Dalwadi; Gaurav (Navi Mumbai, IN),
Shah; Brijesh I (Navi Mumbai, IN), Khosaya;
Nekiram (Navi Mumbai, IN) |
Applicant: |
Name |
City |
State |
Country |
Type |
Reliance Jio Infocomm Limited |
Mumbai |
N/A |
IN |
|
|
Assignee: |
Jio Platforms Limited (Gujarat,
IN)
|
Family
ID: |
1000006469338 |
Appl.
No.: |
17/261,388 |
Filed: |
July 16, 2019 |
PCT
Filed: |
July 16, 2019 |
PCT No.: |
PCT/IB2019/056050 |
371(c)(1),(2),(4) Date: |
January 19, 2021 |
PCT
Pub. No.: |
WO2020/016765 |
PCT
Pub. Date: |
January 23, 2020 |
Prior Publication Data
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|
|
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Document
Identifier |
Publication Date |
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US 20210345129 A1 |
Nov 4, 2021 |
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Foreign Application Priority Data
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|
|
|
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Jul 17, 2018 [IN] |
|
|
201821026563 |
|
Current U.S.
Class: |
1/1 |
Current CPC
Class: |
H04W
16/18 (20130101) |
Current International
Class: |
H04W
16/18 (20090101) |
References Cited
[Referenced By]
U.S. Patent Documents
Foreign Patent Documents
Other References
International Search Report, PCT/IB2019/056050, filed Jul. 16,
2019, 3 pages. cited by applicant .
Written Opinion of the International Searching Authority, dated
Nov. 29, 2019, 5 pages. cited by applicant.
|
Primary Examiner: Liu; Jung
Attorney, Agent or Firm: Finnegan, Henderson, Farabow,
Garrett & Dunner
Claims
We claim:
1. A method of 3D propagation modelling for planning of a radio
network, the method being performed by a network entity, the method
comprising: collecting, by a processor, one or more first network
parameters of a radio network across one or more radio network
sites in a first geographical region, wherein the one or more first
network parameters are collected from a Radio Frequency (RF)
scanner and from at least one user equipment (UE) simultaneously;
performing, by the processor, a prediction analysis for planning of
the network across the one or more radio network sites, wherein the
prediction analysis is performed by using a Continuous-Wave (CW)
based 3D propagation model with the collected one or more first
network parameters, wherein based on the prediction analyses, one
or more second network parameters for each of the one or more radio
network sites are generated; and optimizing, by the processor, the
3D propagation model used for the planning of the radio network in
the first geographical region, by tuning one or more propagation
path loss parameters of the 3D propagation model to compensate for
propagation path losses associated with a Line-of-sight (LOS)
transmission and a Non-line-of-sight (NLOS) transmission in the
radio network, the tuning being performed based on a comparison of
the generated one or more second network parameters with the at
least one Key Point Indicator (KPI) of the 3D propagation
model.
2. The method as claimed in claim 1 comprising: determining a
Continuous-Wave (CW) based 3D propagation model for a second
geographical region based on the 3D model of the first geographical
region, wherein the second geographical region is having a similar
clutter type and geography as of the first geographical region, and
wherein the one or more propagation path loss parameters of the 3D
propagation model of the second geographical region are tuned for
compensating propagation path losses associated with the LOS
transmission and the NLOS transmission, based on one or more UE
parameters collected periodically from a UE present in the radio
network across the second geographical region.
3. The method as claimed in claim 1, wherein the one or more
network parameters are collected using a RF scanner, and wherein
the one or more first network parameters comprising at least
Reference Signals Received Power (RSRP) data and Physical Cell
Identity (PCI) of a serving cell and neighbouring cells across each
of the one or more radio network sites.
4. The method as claimed in claim 1, wherein the one or more first
network parameters are collected along a plurality of drive route
maps which are created around the one or more radio network
sites.
5. The method as claimed in claim 1, wherein the planning of the
radio network comprising at least one of: configuring of the one or
more radio network sites, configuring of Reference Signal Energy
per Resource Element (RS-EPRE) parameter of the radio network
across the one or more radio network sites, configuring of Physical
Cell Identity (PCI) of cells across the radio network, configuring
of antenna patterns of one or more antennas facilitating the radio
network, and/or configuring of site physical parameters including
E-Tilt, M-Tilt and Azimuth of the one or more radio network
sites.
6. The method as claimed in claim 1 comprising: processing a
pre-generated 3D map data along with the collected one or more
first network parameters, using the 3D propagation model;
generating, using the 3D propagation model, a scanner drive data
for the one or more radio network sites based on said processing of
the pre-generated 3D map data along with the collected one or more
network parameters; and determining predicted values depending on
the generated scanner drive data.
7. The method as claimed in claim 6, wherein the prediction
analysis is performed by using the 3D propagation model with the
collected one or more network parameters and with the determined
predicted values.
8. The method as claimed in claim 6, wherein the scanner drive data
comprises drive samples which are obtained from the one or more
first network parameters, based on a comparison of the one or more
first network parameters with predetermined reference values.
9. The method as claimed in claim 1, wherein the at least one KPI
comprises: Mean Error (ME), Standard Deviation (SD), and
Correlation Factor (CF) for each of the one or more radio network
sites.
10. The method as claimed in claim 9, comprising: comparing the
generated one or more second network parameters with the at least
one KPI; determining a correction factor for the one or more first
network parameters, based on said comparison; and tuning the one or
more propagation path loss parameters by correcting the one or more
propagation path loss parameters based on the correction factor, so
that the at least one KPI of the 3D propagation model matches at
least one pre-stored reference KPI.
11. A network entity [100] for 3D propagation modelling for
planning of a radio network, the network entity [100] comprising: a
processor [132] configured to: collect one or more first network
parameters of a radio network across one or more radio network
sites in a first geographical region, wherein the one or more first
network parameters are collected from a Radio Frequency (RF)
scanner and from at least one user equipment (UE) simultaneously;
perform a prediction analysis for planning of the network across
the one or more radio network sites, wherein the prediction
analysis is performed by using a Continuous-Wave (CW) based 3D
propagation model with the collected one or more first network
parameters, wherein based on the prediction analyses, one or more
second network parameters for each of the one or more radio network
sites are generated; and optimize the 3D propagation model used for
the planning of the radio network in the first geographical region,
by tuning one or more propagation path loss parameters of the 3D
propagation model to compensate for propagation path losses
associated with a Line-of-sight (LOS) transmission and a
Non-line-of-sight (NLOS) transmission in the radio network, the
tuning being performed based on a comparison of the generated one
or more second network parameters with the at least one Key Point
Indicator (KPI) of the 3D propagation model.
12. The network entity [100] as claimed in claim 11, wherein the
processor [132] is configured to: determine a Continuous-Wave (CW)
based 3D propagation model for a second geographical region based
on the 3D model of the first geographical region, wherein the
second geographical region is having a similar clutter type and
geography as of the first geographical region, and wherein the one
or more propagation path loss parameters of the 3D propagation
model of the second geographical region are tuned for compensating
propagation path losses associated with the LOS transmission and
the NLOS transmission, based on one or more UE parameters collected
periodically from a UE present in the radio network across the
second geographical region.
13. The network entity [100] as claimed in claim 11, wherein the
one or more network parameters are collected using a RF scanner,
and wherein the one or more first network parameters comprising at
least Reference Signals Received Power (RSRP) data and Physical
Cell Identity (PCI) of a serving cell and neighbouring cells across
each of the one or more radio network sites.
14. The network entity [100] as claimed in claim 11, wherein the
one or more first network parameters are collected along a
plurality of drive route maps which are created around the one or
more radio network sites.
15. The network entity [100] as claimed in claim 11, wherein the
planning of the radio network comprising at least one of:
configuring of the one or more radio network sites, configuring of
Reference Signal Energy per Resource Element (RS-EPRE) parameter of
the radio network across the one or more radio network sites,
configuring of Physical Cell Identity (PCI) of cells across the
radio network, configuring of antenna patterns of one or more
antennas facilitating the radio network, and/or configuring of site
physical parameters including E-Tilt, M-Tilt and Azimuth of the one
or more radio network sites.
16. The network entity [100] as claimed in claim 11, wherein the
processor [132] is configured to: process a pre-generated 3D map
data along with the collected one or more first network parameters,
using the 3D propagation model; generate, using the 3D propagation
model, a scanner drive data for the one or more radio network sites
based on said processing of the pre-generated 3D map data along
with the collected one or more network parameters; and determine
predicted values depending on the generated scanner drive data.
17. The network entity [100] as claimed in claim 16, wherein the
prediction analysis is performed by using the 3D propagation model
with the collected one or more network parameters and with the
determined predicted values.
18. The network entity [100] as claimed in claim 16, wherein the
scanner drive data comprises drive samples which are obtained from
the one or more first network parameters, based on a comparison of
the one or more first network parameters with predetermined
reference values.
19. The network entity [100] as claimed in claim 11, wherein the at
least one KPI comprises: Mean Error (ME), Standard Deviation (SD),
and Correlation Factor (CF) for each of the one or more radio
network sites.
20. The network entity [100] as claimed in claim 19, wherein the
processor [132] is configured to: compare the generated one or more
second network parameters with the at least one KPI; determine a
correction factor for the one or more first network parameters,
based on said comparison; and tune the one or more propagation path
loss parameters by correcting the one or more propagation path loss
parameters based on the correction factor, so that the at least one
KPI of the 3D propagation model matches at least one pre-stored
reference KPI.
Description
TECHNICAL FIELD
The present invention relates generally to wireless network, and
more particularly, to 3D propagation modelling for planning of a
radio network.
BACKGROUND
The following description of related art is intended to provide
background information pertaining to the field of the disclosure.
This section may include certain aspects of the art that may be
related to various features of the present disclosure. However, it
should be appreciated that this section be used only to enhance the
understanding of the reader with respect to the present disclosure,
and not as admissions of prior art.
Today with the advent of wireless technology like GSM, EDGE, HSPA,
LTE, and the like, all communications in a wireless network provide
various communication services such as voice, video, data,
advertisement, content, messaging, broadcasts, etc. One example of
such a network is the Evolved Universal Terrestrial Radio Access
(E-UTRA) which is a radio access network standard meant to be a
replacement of the UMTS and HSDPA/HSUPA technologies specified in
3GPP releases 5 and beyond. E-UTRA is the air interface of 3GPP's
Long Term Evolution (LTE) upgrade path for mobile networks. Unlike
HSPA, LTE's E-UTRA is an entirely new air interface system,
unrelated to and incompatible with W-CDMA. It provides higher data
rates, lower latency and is optimized for packet data. The UMTS,
which is the successor to Global System for Mobile Communications
(GSM) technologies, currently supports various air interface
standards, such as Wideband-Code Division Multiple Access (W-CDMA),
Time Division-Code Division Multiple Access (TD-CDMA), and Time
Division-Synchronous Code Division Multiple Access (TD-SCDMA). The
UMTS also supports enhanced 3G data communications protocols, such
as High-Speed Packet Access (HSPA), which provides higher data
transfer speeds and capacity to associated UMTS networks. With the
capacity and higher data transfer speeds, there are many problems
associated with the cells and the optimization of the cells.
In 4G cellular deployment, macro cells, along with various small
cells, are planned to provide coverage and capacity solution across
the target area. Thus, inter-site distance becomes narrower for LTE
network, particularly with band 40 than 2G/3G radio access network.
Further, a greater number of sites/eNodeBs are required to mitigate
on-going data demand in the LTE network, which creates dense to
hyper-dense radio access network in big cities.
The Radio Frequency (RF) characteristics (e.g., RF propagation)
define the area of coverage for a cell tower. A sector is a
geographical area, having a distinct size and shape, covered by a
single antenna. For example, a service provider may use several
antennas to provide 360 degrees of RF coverage for a cell. A
service provider may use one cell tower to provide wireless
communication services to subscribers for a few tens of meters
radius in urban settings, while for a rural area, the service
provider may use one cell tower for an area of several square
miles. To ensure a good quality of experience for subscribers and
maximum usage of the network, every service operator needs a
complete view of the network and the ability to accurately analyze
different deployment scenarios. The accuracy of RF Planning has a
direct impact on how the network scenario can be optimized in order
to maximize network capacity, coverage and network performance. The
calibration of RF planning is often required when deploying in new
frequency bands and new geographical areas.
Today, most of the operators perform the initial RF planning based
on link budget and standard propagation model with low-resolution
map data. The RF planning is a multi-step process and iterative in
nature and is a very vital step for a wireless communication
technology and is carried out manually by changing physical
parameters of a site, as per the conventional approach. LTE radio
network planning also works just like other cellular technologies.
Whenever new cellular technology is considered for mass deployment,
many RF parameters are subjected to a tuning process with a view to
find out an optimum value. However, this phase is time-consuming
and is expensive. Therefore, before commercial deployment, if an
extensive simulation is carried out, then the tuning phase can be
facilitated in numerous ways. Hence, running simulation before mass
commercial deployment minimizes the cost with a greater extent.
Clearly, optimizing vital parameters in a least possible time is a
challenging issue for network operators. The RF planning tool based
prediction is an essential requirement in the process of network
planning.
The conventional techniques for modeling RF propagation are based
on either empirical measurements or ray-tracing methods. Empirical
methods, for example, are based solely on extensive measurements
made in generic settings at different sites, and are therefore
dependent on the site locations, the measurement methods,
measurement frequencies, and the like. In particular, the signal
strength is measured at a number of different locations and at
different heights, with different empirical models used for
different generic propagation problems, e.g. for rural, suburban,
and urban propagation environments. However, there is no
consideration of details of the propagation environment, e.g.
actual building and street locations and configurations. Therefore,
while empirical methods can predict order-of-magnitude of average
propagation loss, they cannot predict fine details about the
propagation channel like dead zones and multipath interference.
Moreover, empirical methods are based on statistics regarding
experiments without sufficient underlying logic to enable
generalization to environments other than those in which the
experiments were conducted.
The method of ray-tracing is a deterministic method which is based
on geometrical optics and the uniform theory of diffraction (UTD)
and can be used to calculate propagation mechanisms such as the
direct (LOS), reflected, transmitted, diffracted, and some combined
rays. The ray-tracing method is not based on extensive
measurements, but rather relies on site-specific information such
as actual geometric and structural information of the building,
e.g. the facade structure and geometry. The basic procedure of the
ray-tracing method is the shooting-and-bouncing ray (SBR) algorithm
involving three basic processes, with the first process launching a
ray. The second process is determining if the ray hits an object in
what is known as the ray-object intersection test. Moreover, the
third is determining whether a ray is received at a receiver
location.
The accuracy of simulation-based data with actual measured data
directly depends upon the accuracy of the clutter map and its
propagation model. Nowadays, there are many empirical propagation
models available. All these can be broadly classified in two ways:
(i) 2D propagation model (ii) 3D propagation model
The 2D propagation models such as Standard Propagation Model (SPM)
are widely used in RF planning; but are less accurate because of
non-consideration of high-resolution clutter map, actual building
height and multipath effects in simulation. Conventionally, CW
measured drive test data is required to develop any propagation
model. But to perform CW measurement in all cities or towns and
calibrating propagation model are time consuming and very expensive
for large scale network deployment.
Standard Propagation Model (SPM) is one of the most popular 2D
propagation model. It is represented by (1).
L.sub.model=K.sub.1+K.sub.2log.sub.10(d)+K.sub.3log.sub.10(H.sub.Txeff)+K-
.sub.4DiffractionLoss+K.sub.5log.sub.10(d)log.sub.10(H.sub.Txeff)+K.sub.6(-
H.sub.Rxeff)+K.sub.clutterf(clutter) (1) Where, K.sub.1--constant
offset (dB) K.sub.2--Multiplying factor for log.sub.10 (d)
d--Distance between transmitter and receiver (m)
K.sub.3--Multiplying factor for log.sub.10 (HTxeff)
H.sub.Txeff--Effective height of the transmitter antenna (m)
K.sub.4--Multiplying factor for diffraction calculation
K.sub.5--Multiplying factor for log.sub.10 (d)log.sub.10
(H.sub.Txeff) K.sub.6--Multiplying factor for log.sub.10
(H.sub.Rxeff) H.sub.Rxeff--Effective UE antenna height (m)
K.sub.clutter--Multiplying factor for f (clutter) f
(clutter)--Average of weighted losses due to clutter
2D propagation model such as SPM uses the raster data and Digital
Terrain Map (DTM). DTM carries the details of geographical terrain
height from sea level. Raster layer comprises the details of
various clutters like dense urban, medium urban, suburban, rural,
low vegetation, dense vegetation, water bodies, open area etc. with
mean clutter height. The resolution of the raster layer could be in
between 5-100 m. This impacts highly on the accuracy of the
prediction performance.
Apart from the above-described drawbacks, one of the drawbacks with
2D propagation model is a separate model requirement for each
morphology. Dense urban, medium urban, suburban and rural etc.
require separately tuned 2D propagation models. Even for metro
cities, the clutter variation is also large, which increases the
number of propagation model tuning requirement for each clutter
type. In an urban environment, SPM based predictions can only
provide an idea of the received signal. Moreover, in densely
populated areas, SPM based predictions may only provide a rough
estimate of the received signal around the base station, as
coverage is highly impacted by surrounding obstacles.
Apart from SPM, 3D propagation model works on 3D ray tracing
phenomenon and consider effects of canyoning, power delay spread,
diffusion and multipath signals. It requires DTM along with either
3D building vector map or raster layer with Digital Height Map
(DHM). The resolution of 3D building vector map and DHM may lie in
between 2-10 m. Ray tracing model also includes reflection,
refraction and diffusion coefficients, which makes it more
precisely accurate and suitable for the urban environment compared
to 2D propagation model. Further, an entire city can have a unique
3D propagation model rather than morphology wise different models
as required in the 2D case. In one example, one of the 3D
propagation models is presented as below. Received
Power=EIRP-(A(far,near)+Blog.sub.10(D)+.gamma.L.sub.DET+L.sub.ANT+L.sub.T-
S+L.sub.C) (2) Where, EIRP--Sum of the transmit power plus the
maximum antenna gain (dBm) D--Tx-Rx distance (m)
L.sub.DET--Deterministic loss from direct-path or from multi-path
combination (dB) L.sub.ANT--Antenna loss from direct-path or from
multi-path combination, including antenna correction (dB)
L.sub.FS--Propagation loss in free-space at a distance 1 m (dB)
L.sub.C--Clutter loss, including clutter attenuation and
in-building propagation loss (dB) A--Additional loss (dB)
(A.sub.near for the receivers in the near area, A.sub.far for the
receivers in the far area) B--Additional loss per distance decade
(dB/dec) (B.sub.near for the receivers in the near area, B.sub.far
for the receivers in the far area) .gamma.--Weight of the
deterministic loss
However, key problems in the current modelling solutions are: 1.
While tuning the propagation path, there are loss parameters for
Line-of-sight (LOS) and Non-line-of-sight (NLOS) in the existing CW
based ray tracing propagation models. 2. The conventional 3D
propagation model cannot create modelling having a similar
geographical area and clutter types without performing any drive
test. 3. It is not possible to provide accurate 3D propagation
models due to channel interfering noise of the measured frequency
channel. 4. The prior arts fail to provide accurate 3D propagation
models that can be tuned automatically based on periodically
captured UE data.
Therefore, there is a need to alleviate drawbacks existing in the
prior arts.
SUMMARY
This section is provided to introduce certain objects and aspects
of the present disclosure in a simplified form that are further
described below in the detailed description. This summary is not
intended to identify the key features or the scope of the claimed
subject matter.
Embodiments of the present disclosure may relate to a method of 3D
propagation modelling for the planning of a radio network. The
method includes collecting, by a processor, one or more first
network parameters of a radio network across one or more radio
network sites in a first geographical region, wherein the one or
more first network parameters are collected from a Radio Frequency
(RF) scanner and from at least one user equipment (UE)
simultaneously. The method further includes performing, by the
processor, a prediction analysis for planning of the network across
the one or more radio network sites, wherein the prediction
analysis is performed by using a Continuous-Wave (CW) based 3D
propagation model with the collected one or more first network
parameters, wherein based on the prediction analyses, one or more
second network parameters for each of the one or more radio network
sites are generated. Further, the method includes optimizing, by
the processor, the 3D propagation model used for the planning of
the radio network in the first geographical region, by tuning one
or more propagation path loss parameters of the 3D propagation
model to compensate for propagation path losses associated with a
Line-of-sight (LOS) transmission and a Non-line-of-sight (NLOS)
transmission in the radio network, the tuning being performed based
on a comparison of the generated one or more second network
parameters with the at least one Key Point Indicator (KPI) of the
3D propagation model.
Further, the embodiments of the present disclosure may relate to a
network entity for 3D propagation modelling for the planning of a
radio network. The network entity includes a processor configured
to collect one or more first network parameters of a radio network
across one or more radio network sites in a first geographical
region, wherein the one or more first network parameters are
collected from a Radio Frequency (RF) scanner and from at least one
user equipment (UE) simultaneously. Further, the processor is
configured to perform a prediction analysis for planning of the
network across the one or more radio network sites, wherein the
prediction analysis is performed by using a Continuous-Wave (CW)
based 3D propagation model with the collected one or more first
network parameters, wherein based on the prediction analysis, one
or more second network parameters for each of the one or more radio
network sites are generated. Furthermore, the processor is
configured to optimize the 3D propagation model used for the
planning of the radio network in the first geographical region, by
tuning one or more propagation path loss parameters of the 3D
propagation model to compensate for propagation path losses
associated with a Line-of-sight (LOS) transmission and a
Non-line-of-sight (NLOS) transmission in the radio network, the
tuning being performed based on a comparison of the generated one
or more second network parameters with the at least one Key Point
Indicator (KPI) of the 3D propagation model.
BRIEF DESCRIPTION OF THE DRAWINGS
The accompanying drawings, which are incorporated herein, and
constitute a part of this disclosure, illustrate exemplary
embodiments of the disclosed methods and systems in which like
reference numerals refer to the same parts throughout the different
drawings. Components in the drawings are not necessarily to scale,
emphasis instead being placed upon clearly illustrating the
principles of the present disclosure. Some drawings may indicate
the components using block diagrams and may not represent the
internal circuitry of each component. It will be appreciated by
those skilled in the art that disclosure of such drawings includes
disclosure of electrical components or circuitry commonly used to
implement such components.
FIG. 1 is a diagram of exemplary components of a network entity, in
accordance with an embodiment of the present invention.
FIG. 2 is a simplified flow diagram of a method of 3D propagation
modelling for the planning of a radio network, in accordance with
an embodiment of the present invention.
FIG. 3 is a simplified flow diagram illustrating the step [204] of
FIG. 2 in detail.
FIG. 4 is a simplified flow diagram illustrating the step [206] of
FIG. 2 in detail.
FIG. 5 is a simplified flow diagram illustrating the step [208] of
FIG. 2 in detail.
FIG. 6 is a simplified flow diagram illustrating obtaining of 3D
propagation models of remaining geographies from a 3D propagation
model of a first geography, in accordance with an embodiment of the
present invention.
FIG. 7 illustrates the prediction and measurement sample
correlation along with 3D propagation model parameters for
Aurangabad city based on the implementation of the present
invention.
FIG. 8 illustrates the prediction and measurement sample
correlation along with 3D propagation model parameters for Kochi
city based on the implementation of the present invention.
FIG. 9 illustrates the prediction and measurement sample
correlation along with 3D propagation model parameters for Jaipur
city based on the implementation of the present invention.
FIG. 10 illustrates prediction and measurement sample correlation
along with 3D propagation model parameters for Dehradun city based
on the implementation of the present invention.
FIG. 11 illustrates the prediction and measurement sample
correlation along with 3D propagation model parameters for Lucknow
city based on the implementation of the present invention.
The foregoing shall me more apparent from the following more
detailed description of the present invention.
DETAILED DESCRIPTION
In the following description, for the purposes of explanation,
various specific details are set forth in order to provide a
thorough understanding of embodiments of the present invention. It
will be apparent, however, that embodiments of the present
invention may be practiced without these specific details. Several
features described hereafter can each be used independently of one
another or with any combination of other features. An individual
feature may not address any of the problems discussed above or
might address only some of the problems discussed above. Some of
the problems discussed above might not be fully addressed by any of
the features described herein. Example embodiments of the present
invention are described below, as illustrated in various drawings
in which like reference numerals refer to the same parts throughout
the different drawings.
The present invention provides a system and method for 3D
propagation modelling wherein automatic tuning of propagation path
loss parameters of a Continuous Wave (CW) based 3D propagation
model for LOS (line of sight) and NLOS (non-line of sight) radio
transmissions in a first geographical area, is performed. Further,
in the present invention, 3D propagation models for remaining
geographies having similar geographical area and clutter types as
the first geographical area, are generated without performing any
drive test while compensating the propagation path loss parameters
of the generated model using periodically measured user equipment
(UE) data. The generated 3D models may be updated dynamically as
the 3D models are developed based on UE data updated from time to
time.
FIG. 1 illustrates a diagram of exemplary components of a network
entity [100]. As shown in FIG. 1, the network entity [100] may
include antennas [110], transceivers (TX/RX) [120], a processing
system [130], and an interface (I/F) [140]. In one embodiment, the
antennas [110] may include one or more directional and/or
omnidirectional antennas. Transceivers [120] may be associated with
the antennas [110] and may include transceiver circuitry for
transmitting and/or receiving symbol sequences in a network, such
as a wireless network, via antennas [110].
The processing system [130] may control an operation of the network
entity [100]. The processing system [130] may also process
information received via the transceivers [120] and/or the
interface [140]. As shown in FIG. 1, the processing system [130]
may include a processor [132] and a memory [134]. In one
embodiment, the processor [132] may include one or more processors,
microprocessors, application specific integrated circuits (ASICs),
field programmable gate arrays (FPGAs), and the like. The processor
[132] may process information received via transceivers [120]
and/or interface [140]. In addition, the processor [132] may
transmit control messages and/or data messages and may cause those
control messages and/or data messages to be transmitted via
transceivers [120] and/or interface [140]. The processor [132] may
also process control messages and/or data messages received from
transceivers [120] and/or interface [140]. In one embodiment, the
memory [134] may include a random access memory (RAM), a read-only
memory (ROM), and/or another type of memory to store data and
instructions that may be used by the processor [132].
In one embodiment, the interface [140] may include one or more
circuitries, such as line cards that allow the network entity to
transmit data to and receive data from another network entity. In
another embodiment, the interface [140] may include I/O interfaces
through which the network entity [100] may receive user inputs
and/or data from a user.
In one embodiment, the network entity [100] may perform certain
operations in response to the processor [132] executing software
instructions of an application contained in a computer-readable
medium, such as memory [134]. A computer-readable medium may be
defined as a physical or logical memory device. A logical memory
device may include memory space within a single physical memory
device or spread across multiple physical memory devices. The
software instructions may be read into memory [134] from another
computer-readable medium or from another device via antennas [110]
and transceivers [120]. The software instructions contained in
memory [134] may cause the processor [132] to perform processes
described herein. Alternatively, hardwired circuitry may be used in
place of or in combination with software instructions to implement
processes described herein. Thus, embodiments described herein are
not limited to any specific combination of hardware circuitry and
software.
Although FIG. 1 shows exemplary components of the network entity
[100], in other embodiments, the network node may contain fewer,
different, differently arranged, or additional components than
depicted in FIG. 1. Alternatively, or additionally, one or more
components of the network entity may perform one or more other
tasks described as being performed by one or more other components
of the network entity.
Referring to FIG. 2, there is illustrated a simplified flow diagram
of a method for 3D propagation modelling for planning of a radio
network. Beginning at step [204], the network entity [100] collects
one or more first network parameters of a radio network across one
or more radio network sites in a first geographical region.
Further, the network entity [100] collects one or more UE
parameters of at least one user equipment (UE) connected to the
radio network. The step [204] of FIG. 2 is further described in
detail in step [204A]-[204E] of FIG. 3.
At step [206], the network entity [100] performs a prediction
analysis for planning of the network across the one or more radio
network sites. The prediction analysis is performed by using a
Continuous-Wave (CW) based 3D propagation model with the collected
one or more first network parameters, wherein based on the
prediction analysis, one or more second network parameters for each
of the one or more radio network sites are generated by the network
entity [100]. The step [206] of FIG. 2 is further described in
detail with reference to steps [206A]-[206G] of FIG. 4.
At step [208], the network entity [100] optimizes the 3D
propagation model used for the planning of the radio network in the
first geographical region, by tuning one or more propagation path
loss parameters of the 3D propagation model to compensate for
propagation path losses associated with a Line-of-sight (LOS)
transmission and a Non-line-of-sight (NLOS) transmission in the
radio network, based on a comparison of the generated one or more
second network parameters with the at least one Key Point Indicator
(KPI) of the 3D propagation model. The step [208] of FIG. 2 is
described in more detail with reference to steps [208A]-[208E] of
FIG. 5.
Referring to FIG. 3, what shows a simplified flow diagram
illustrating the step [204] of FIG. 2 in detail. At step [204A],
the network entity [100] selects a predetermined number of on-air
sites (i.e., radio network sites) covering all morphologies such as
Dense urban (DU), medium urban (MU), suburban (SU), and rural (RU)
of the first geographical region, such as a city. For example, the
network entity [100] may select at least 20% radio network sites of
a selected city out of total scope cities. Subsequently, at step
[204B], the network entity [100] determines whether the radio
network sites across all the morphologies are less than the
predetermined number of sites, for example, less than 20%. In an
event, the number of radio network sites is less than the
predetermined number, the network entity [100] repeats the step
[204A]. In an event, the radio network sites across all the
morphologies are greater than the predetermined number of sites,
then step [204C] is performed by the network entity [100].
At step [204C], drive route maps are created in and around the
first geographical area to collect drive map data, wherein the
drive map data may include at least the one or more network
parameters and the one or more UE parameters. In one embodiment,
the drive map data may be in the form of a Spider Web in which
there may be a dense route near a radio network site and an open
route far away from the radio network site. At step [204D], the
network entity [100] may then collect the one or more network
parameters including but not limited to Reference Signal Received
Power (RSRP) data samples, for the radio network sites using an RF
scanner. Further, the one or more network parameters may include
Physical Cell Identity (PCI) information for a serving cell as well
as neighbour cells across each of the one or more radio network
sites. At step [204D], the network entity [100] may collect the one
or more network parameters from an RF scanner, and at step [204E],
the network entity [100] may collect the one or more network
parameters from at least one UE.
Referring now to FIG. 4, that shows a simplified flow diagram
illustrating the step [206] of FIG. 2 in detail. At step [206A],
the network entity [100] receives an input data, wherein the input
data is a pre-generated 3D map data. In one embodiment, the input
data may include 3D building vector data of the first geographical
region, Digital Terrain Model (DTM) having clutter height data of
the first geographical region, an available Continuous Wave (CW)
based 3D propagation model of any geographical region other than
the first geographical region, database of on-air radio network
sites of the first geographical region. Further, the collected one
or more first network parameters are combined with the received
input data, by the network entity [100]. At step [206B], a planning
module for planning of the radio network across the radio network
sites is created, wherein the planning of the radio network
comprising at least one of: configuring of the one or more radio
network sites, configuring of Reference Signal Energy Per Resource
Element (RS-EPRE) parameter of the radio network across the one or
more radio network sites, configuring of Physical Cell Identity
(PCI) of cells across the radio network, configuring of antenna
patterns of one or more antennas facilitating the radio network,
and/or configuring of site physical parameters of the one or more
radio network sites. The physical site parameters may include but
are not limited to E-Tilt, M-Tilt and Azimuth.
Next, at steps [206C] and [206D], the input data is imported into
the module created at step [206B]. At step [206E], the network
entity [100] generates a scanner drive data for the one or more
radio network sites based on the processing of the pre-generated 3D
map data (i.e., the input data) along with the collected one or
more first network parameters. Subsequently, at step [206F], the
network entity [100] filters the generated scanner drive data to
extract drive samples for generating predicted values, wherein the
drive samples are extracted based on a comparison of the one or
more first network parameters with predetermined reference values.
For example, samples which are falling on buildings, having RSRP
values <-120 dBm, having lower RSRP values near to the site and
high RSRP value far away from the mapped PCI site, and being
collected from more than 1 Km from the mapped PCI site, are
filtered out from the generated drive samples. Thereafter, at step
[206G], the network entity [100] performs the prediction analysis
by using the 3D propagation model with the collected one or more
network parameters and with the determined predicted values to
generate the one or more second network parameters. In one
embodiment, the one or more second network parameters correspond to
parameters generated by prediction analysis between measured and
predicted data on a drive path of the RF scanner of all PCI mapped
radio network sites.
Referring now to FIG. 5, that shows a simplified flow diagram
illustrating the step [208] of FIG. 2 in detail. At step [208A],
the network entity [100] performs cell-wise validation of key-point
indicator (KPI)s of the 3D propagation model, wherein the KPIs may
include but not limited to Mean Error (ME), Standard Deviation (SD)
and Correlation Factor (CF). The network entity [100] determines
the Mean Error (ME), Standard Deviation (SD) and Correlation Factor
(CF) samples for each of the radio network site, wherein the ME,
SD, and CF are determined for the serving cell and the neighbouring
cells in each of the radio network site. At step [208B], the
network entity [100] determines whether cumulative prediction
analysis of the radio network sites, as described in step [206],
matches one or more pre-stored reference KPIs of the 3D propagation
model. In an event, the cumulative prediction analysis matches the
pre-stored reference KPIs, the step [208D] is performed, else the
step [208C] is performed. At step [208C], the network entity [100]
filters samples of the radio network sites having a high value of
mean error, low correlation and high standard deviation, after the
prediction analysis is performed.
At step [208D], the network entity [100] determines correction
factor .alpha. and .beta. values, respectively for the one or more
first network parameters collected from the RF scanner and the UE.
The correction factor is determined after calculating an error
based on the difference between the predicted RSRP and the measured
RSRP. At step [208E], the network entity [100] performs tuning of
one or more propagation path loss parameters of the 3D propagation
model by correcting the one or more propagation path loss
parameters based on the determined correction factor .alpha. and
.beta. so that the KPIs of the 3D propagation model matches the at
least one pre-stored reference KPI. The one or more propagation
path loss parameters are tuned to compensate for propagation path
losses associated with a Line-of-sight (LOS) transmission and a
Non-line-of-sight (NLOS) transmission in the radio network. In one
exemplary embodiment, the one or more propagation path loss
parameters are tuned so that the KPIs of the 3D propagation model
matches Mean Error (ME)=0, Standard Deviation (SD)<8 dB and
Correlation Factor (CF)>0.7.
Referring to FIG. 6, a simplified flow diagram for generating 3D
propagation models of other geographies based on a generated 3D
propagation model of a first geography, is disclosed. At step 602,
the network entity [100] may repeat the aforementioned steps
204A-204E of FIG. 3, steps 206A-206G of FIG. 4 and steps 208A-208E
of FIG. 5 to make a 3D propagation model of the pre-determined
number of geographical regions other than the first geographical
region. At step [604], the network entity [100], may create a 3D
propagation model of other geographical regions having similar
geography and clutter type as of the first geographical region,
while tuning the propagation path loss components of the generated
model based on UE data which is periodically received from a user
equipment (UE), without requiring to perform a drive test using the
RF scanner.
For example, assuming that RF scanner based drive data is collected
for `n` number of radio network sites. The mean value of RSRP error
between predicted data by an RF planning tool and scanner based
measured data for n sites are a.sub.1, a.sub.2, a.sub.3, . . . ,
a.sub.n and their correlation factors with RF prediction data are
c.sub.1, c.sub.2, c.sub.3, . . . , c.sub.n. Further, the standard
deviation of n sites are .sigma..sub.1, .sigma..sub.2,
.sigma..sub.3, . . . , .sigma..sub.n. As per the method described
above in the disclosure, overall mean error for drive data of the
entire city is derived by,
.alpha..times..mu..times..times..mu. ##EQU00001## Where,
.mu..sub.i.di-elect cons.{0,1} .mu..sub.i=0 for c.sub.i<0.5 and
.mu..sub.i=1 for 0.5<c.sub.i<1, i=1,2,3, . . . , n wherein,
equation (3) shows that the value of .alpha. is within the
confidence interval of correlation.
In one aspect, overall Standard deviation (SD) will be derived
by,
.times..mu..times..times..sigma..alpha. ##EQU00002##
Based on (2), (3) and (4) and considering .beta. as a UE data
correction factor with respect to RF scanner received power, the
near field and far field offset correction factor `A` in a 3D
propagation model will be replaced by `A1` as below:
A1=A(far,near)+.alpha.+.beta. (5)
Therefore, modified (2) and (5) for received power based on the
above methodology will be: Received
Power.sub.new=EIRP-(A1+Blog.sub.10(D)+.gamma.L.sub.DET+L.sub.ANT+L.sub.FS-
+L.sub.C) (6)
The above equation (6) may be used for 3D predictions with the
condition of Standard deviation (SD)<8, otherwise, convergence
cannot be achieved within confidence interval because of larger
deviation from mean path.
Referring to FIG. 7-FIG. 11, implementation of the system and
method of the present invention is described. The system and method
of the present invention has been implemented for 3D propagation
modelling for the planning of a radio network across five different
cities Aurangabad, Kochi, Jaipur, Dehradun, and Lucknow,
respectively. A comparison between values based on tuned 3D
propagation model as per the current invention and data obtained
from an RSRP drive test of these cities is shown. FIG. 7 shows
prediction and measurement sample correlation along with 3D
propagation model parameters for Aurangabad city. FIG. 8 shows the
prediction and measurement sample correlation along with 3D
propagation model parameters for Kochi city. FIG. 9 shows the
prediction and measurement sample correlation along with 3D
propagation model parameters for Jaipur city. FIG. 10 shows
prediction and measurement sample correlation along with 3D
propagation model parameters for Dehradun city. FIG. 11 shows
prediction and measurement sample correlation along with 3D
propagation model parameters for Lucknow city.
The units, interfaces, modules, and/or components depicted in the
figures and described herein may be present in the form of a
hardware, a software and a combination thereof. Connection/s shown
between these units/components/modules/interfaces in the exemplary
system architecture [100A] may interact with each other through
various wired links, wireless links, logical links and/or physical
links. Further, the units/components/modules/interfaces may be
connected in other possible ways.
While considerable emphasis has been placed herein on the disclosed
embodiments, it will be appreciated that many embodiments can be
made and that many changes can be made to the embodiments without
departing from the principles of the present invention. These and
other changes in the embodiments of the present invention will be
apparent to those skilled in the art, whereby it is to be
understood that the foregoing descriptive matter to be implemented
is illustrative and non-limiting.
* * * * *